Klasifikasi Penyakit Hipertensi dan Diabetes Berbasis Web Pada Klinik Pratama Rumkitban 01.08.03 Batam

  • Mira Chandra Kirana Politeknik Negeri Batam
  • Michel K Politeknik Negeri Batam
Keywords: Algoritma C45, Classification, Diabetes, Hypertension, Tree Decision

Abstract

The management of outpatient medical records at the Rumkitban Primary Clinic 01.08.03 Batam is still manual and causes many limitations and problems. This problem resulted in the inability of the clinic to run the Chronic Disease Treatment Program (PROLANIS) organized by BPJS-Health. The purpose of the study is to facilitate data processing and then from that data it can be used to classify hypertension and diabetes then the results of the classification are displayed in graphical form. This study discusses 2 diseases, namely hypertension and diabetes. The system uses the C45 Tree Decision Algorithm for automatic data processing. The attributes used are glucose, diastolic, systolic, insulin, and age to support the decision-making system. The system can make a decision whether the patient has hypertension, diabetes or not. The results of this study are the accuracy of classification accuracy in the system for hypertension shows 16.667% accuracy and 83.333% accuracy is not correct, then the calculation of diabetes classification accuracy shows 96.667% accuracy, and 3.333% accuracy is incorrect. This system is integrated with Mysql database to store the results.

Downloads

Download data is not yet available.

References

B. Kesehatan, “Panduan Praktis Prolanis,” 2014. [Online]. Available: https://bpjs-kesehatan.go.id/bpjs/dmdocuments/06-PROLANIS.pdf. [Accessed 20 Juli 2021].

A. Rika, H. Kusnanto and W. Istiono, “Analisis Kesuksesan Implementasi Rekam Medis Elektronik Di RS Universitas Gadjah Mada,” Jurnal Sistem Informasi, 2017.

L. Pereira, R. Rijo, C. Silva and R. Martinho, “Text Mining Applied to Electronic Medical Records: A Literature Review,” International Journal of E-Health and Medical Communications, pp. 1-18, 2015.

KBBI, “KBBI Daring,” Badan Pengembangan dan Pembinaan Bahasa, Kementerian Pendidikan dan Kebudayaan Republik Indonesia, 2016. [Online]. Available: https://kbbi.kemdikbud.go.id/entri/web. [Accessed 08 Februari 2021].

A. J. N. Susanto, Klasifikasi Penyakit Diabetes Mellitus Menggunakan Algoritma Decision Tree C4.5, Batam: Politeknik Negeri Batam, 2018.

M. S. Efendi and H. A. Wibawa, Prediksi Penyakit Diabetes Menggunakan Algoritma ID3 dengan Pemilihan Atribut Terbaik, vol. VI, Purwokerto: JUITA, 2018, pp. 1-8.

F. Hermawati, Data Mining, Yogyakarta: Andi, 2013.

F. S. Sulaeman and M. A. Rilmansyah, “Aplikasi Penerapan Algoritma C45 untuk Memprediksi Kelulusan,” Jurnal Media Teknik & Sistem Industri, vol. 5, pp. 41-54, 2021.

K. RI, Pusdatin Hari Hipertensi Sedunia, Jakarta Selatan: Kementrian Kesehatan RI, 2014, pp. 1-8.

K. RI, Pusdatin Hari Diabetes Sedunia, Jakarta Selatan: Kementrian Kesehatan RI, 2018.

Published
2021-07-21
How to Cite
[1]
M. Kirana and M. K, “Klasifikasi Penyakit Hipertensi dan Diabetes Berbasis Web Pada Klinik Pratama Rumkitban 01.08.03 Batam”, JAIC, vol. 5, no. 1, pp. 74-86, Jul. 2021.
Section
Articles

Most read articles by the same author(s)